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Journals and Conferences

Key Phrases

We present a simple algorithm for computing the PageRank (stationary distribution) of the stochastic Google matrix G. The algorithm lumps all dangling nodes into a single node. We express lumping as a similarity transformation of G, and show that the PageRank of the nondangling nodes can be computed separately from that of the dangling nodes. The algorithm… (More)

We consider the problem of how to group information when multiple similarities are known. For a group of people, we may know their education, geographic location and family connections and want to cluster the people by treating all three of these similarities simultaneously. Our approach is to store each similarity as a slice in a tensor. The similarity… (More)

Ergodicity coefficients for stochastic matrices determine inclusion regions for subdominant eigenvalues; estimate the sensitivity of the stationary distribution to changes in the matrix; and bound the convergence rate of methods for computing the stationary distribution. We survey results for ergodicity coefficients that are defined by p-norms, for… (More)

We present a simple algorithm for computing the PageRank (stationary distribution) of the stochastic Google matrix G. The algorithm lumps all dangling nodes into a single node. We express lumping as a similarity transformation of G and show that the PageRank of the nondangling nodes can be computed separately from that of the dangling nodes. The algorithm… (More)